Learning feature weights from positive cases

Sidath Gunawardena, Rosina O. Weber, Julia Stoyanovich

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    The availability of new data sources presents both opportunities and challenges for the use of Case-based Reasoning to solve novel problems. In this paper, we describe the research challenges we faced when trying to reuse experiences of successful academic collaborations available online in descriptions of funded grant proposals. The goal is to recommend the characteristics of two collaborators to complement an academic seeking a multidisciplinary team; the three form a collaboration that resembles a configuration that has been successful in securing funding. While seeking a suitable measure for computing similarity between cases, we were confronted with two challenges: a problem context with insufficient domain knowledge and data that consists exclusively of successful collaborations, that is, it contains only positive instances. We present our strategy to overcome these challenges, which is a clustering-based approach to learn feature weights. Our approach identifies poorly aligned cases, i.e., ones that violate the assumption that similar problems have similar solutions. We use the poorly aligned cases as negatives in a feedback algorithm to learn feature weights. The result of this work is an integration of methods that makes CBR useful to yet another context and in conditions it has not been used before.

    Original languageEnglish (US)
    Title of host publicationCase-Based Reasoning Research and Development - 21st International Conference, ICCBR 2013, Proceedings
    Pages134-148
    Number of pages15
    DOIs
    StatePublished - 2013
    Event21st International Conference on Case-Based Reasoning Research and Development, ICCBR 2013 - Saratoga Springs, NY, United States
    Duration: Jul 8 2013Jul 11 2013

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume7969 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other21st International Conference on Case-Based Reasoning Research and Development, ICCBR 2013
    CountryUnited States
    CitySaratoga Springs, NY
    Period7/8/137/11/13

    Keywords

    • Case Alignment
    • Case Cohesion
    • Density Clustering
    • Multidisciplinary Collaboration
    • Recommender Systems
    • Single Class Learning
    • Subspace Clustering

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

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  • Cite this

    Gunawardena, S., Weber, R. O., & Stoyanovich, J. (2013). Learning feature weights from positive cases. In Case-Based Reasoning Research and Development - 21st International Conference, ICCBR 2013, Proceedings (pp. 134-148). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7969 LNAI). https://doi.org/10.1007/978-3-642-39056-2_10